Model-free adaptive tracking control for networked nonlinear systems with data dropout

The data-driven tracking control problem is investigated for networked nonlinear systems subjected to data dropout. The desired time-varying output trajectory is considered in this article, which is more general than the constant trajectory. In order to improve the tracking performance, the change rate of tracking error is additionally introduced to the performance index to design model-free adaptive controller. Accordingly, more adjustable parameters are introduced into the controller. With the help of the approximation ability of RBFNN, an RBFNN estimation algorithm is designed to compensate for the adverse effects of data dropout. It is shown that the proposed controller can guarantee the system output to track the desired time-varying output trajectory. Finally, two numerical simulations are provided to illustrate the effectiveness of the proposed approach.